import os
import numpy as np
import pandas as pd

# TensorFlow
import tensorflow as tf
from tensorflow.keras import layers, models

# cv
import cv2

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## progressbar
import progressbar

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib import cm
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Natural Processes Analytics and Modeling

Fire Dataset

Context

The dataset was created by my team during the NASA Space Apps Challenge in 2018, the goal was to use the dataset to develop a model that can recognize the images with fire.

Content

Data was collected to train a model to distinguish between the images that contain the fire (fire images) and regular images (non-fire images), so the whole problem was binary classification. Data is divided into 2 folders, fire images folder contains 755 outdoor-fire images some of them contains heavy smoke, the other one is non-fire images which contain 244 nature images (eg: forest, tree, grass, river, people, foggy forest, lake, animal, road, and waterfall). Hint: Data is skewed, which means the 2 classes(folders) don't have an equal number of samples, so make sure that you have a validation set with an equally sized number of images per class (eg: 40 images of both fire and non-fire classes).

Dataset

def Path_Tree(PATH, Extension):
    Out = {}
    sep = ' ' * 3
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    title = PATH.split('\\')[-1]
    print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1) + Style.RESET_ALL)
    print(Back.BLACK + Fore.CYAN + Style.NORMAL + title+':'+ Style.RESET_ALL)
    print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1)+ Style.RESET_ALL)
    i = 0
    C = ['Red', 'Magenta']
    for entry in os.listdir(PATH):
        if os.path.isdir(PATH):
            print('└──',BACK[C[i]] + Fore.BLACK + Style.NORMAL + entry+':'+ Style.RESET_ALL)
            Sub = os.path.join (PATH, entry)
            List = os.listdir(Sub)
            List = [x for x in List if x.endswith(Extension)]
#             Out[entry] = [os.path.join(Sub, x) for x in List]
            Out[entry] = List
            print(2* sep, Back.YELLOW + Fore.BLACK + Style.NORMAL +
                          '%i %s files' % (len(List), List[0].split('.')[-1].upper()) + Style.RESET_ALL)
            print(2* sep, ', '.join(List[:5]) + ', ...')
            i+=1
    return Out

Path = 'fire_dataset'
Files_dict = Path_Tree(Path, '.png')

=============
fire_dataset:
=============
└── fire_images:
       755 PNG files
       fire.1.png, fire.10.png, fire.100.png, fire.101.png, fire.102.png, ...
└── non_fire_images:
       244 PNG files
       non_fire.1.png, non_fire.10.png, non_fire.100.png, non_fire.101.png, non_fire.102.png, ...

Temp = list(Files_dict.keys())
Labels_dict = dict(zip(Temp, [x.replace('_images','').replace('_',' ').title().replace('Non ','Non-') for x in Temp]))
Dataset = pd.DataFrame()
for c in list(Files_dict.keys()):
    Temp = pd.DataFrame()
    Temp['File'] = [c + '/' + x for x in Files_dict[c]]
    Temp['Label'] = c.replace('_images','').replace('_',' ').title().replace('Non ','Non-')
    Dataset = pd.concat([Dataset, Temp])
del Temp
display(Dataset)

	File	Label
0	fire_images/fire.1.png	Fire
1	fire_images/fire.10.png	Fire
2	fire_images/fire.100.png	Fire
3	fire_images/fire.101.png	Fire
4	fire_images/fire.102.png	Fire
...	...	...
239	non_fire_images/non_fire.95.png	Non-Fire
240	non_fire_images/non_fire.96.png	Non-Fire
241	non_fire_images/non_fire.97.png	Non-Fire
242	non_fire_images/non_fire.98.png	Non-Fire
243	non_fire_images/non_fire.99.png	Non-Fire

999 rows × 2 columns

batch_size = 128
Img_Height = 180
Img_Width = 180

train_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= Path, validation_split=0.2, subset="training",
                                                               seed=123, image_size=(Img_Height, Img_Width),
                                                               batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= Path, validation_split=0.2, subset="validation",
                                                             seed=123, image_size=(Img_Height, Img_Width),
                                                             batch_size=batch_size)

Found 999 files belonging to 2 classes.
Using 800 files for training.
Found 999 files belonging to 2 classes.
Using 199 files for validation.

fig, ax = plt.subplots(4, 4 , figsize = (16, 16))
ax = ax.ravel()
class_names = train_ds.class_names
for images, labels in train_ds.take(1):
    for i in range(len(ax)):
        _ = ax[i].imshow(images[i].numpy().astype("uint8"))
        if labels[i].numpy() == 0:
            Color = 'Red'
        else:
            Color = 'SeaGreen'
        _ = ax[i].set_title(Labels_dict[class_names[labels[i]]],
                            fontweight='bold', fontsize = 14, color = Color)
        _ = ax[i].axis("off")
        _ = ax[i].set_aspect(1)

Modeling: Keras Multi-layer Perceptron (MLP) for Image Classifications

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the Cross-Entropy Loss to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

model = models.Sequential([layers.experimental.preprocessing.Rescaling(1./255, input_shape=(Img_Height, Img_Width, 3)),
                           layers.Conv2D(16, 3, padding='same', activation='relu'),
                           layers.MaxPooling2D(),
                           layers.Conv2D(32, 3, padding='same', activation='relu'),
                           layers.MaxPooling2D(),
                           layers.Conv2D(64, 3, padding='same', activation='relu'),
                           layers.MaxPooling2D(),
                           layers.Flatten(),
                           layers.Dense(128, activation='relu'),
                           layers.Dense(len(Labels_dict))])
model.summary()
tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, expand_nested = True)

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
rescaling (Rescaling)        (None, 180, 180, 3)       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 180, 180, 16)      448       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 90, 90, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 90, 90, 32)        4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 45, 45, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 45, 45, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 22, 22, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 30976)             0         
_________________________________________________________________
dense (Dense)                (None, 128)               3965056   
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 258       
=================================================================
Total params: 3,988,898
Trainable params: 3,988,898
Non-trainable params: 0
_________________________________________________________________

Compiling and fitting the model

# Number of iterations
IT = 21

model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

# Training the model
history = model.fit(train_ds, validation_data=val_ds, epochs=IT, verbose = 0)

def Search_List(Key, List): return [s for s in List if Key in s]

Metrics_Names = {'loss':'Loss', 'accuracy':'Accuracy', 'mae':'MAE', 'mse':'MSE', 'recall': 'Recall'}

def Table_modify(df, Metrics_Names = Metrics_Names):
    df = df.rename(columns = Metrics_Names)
    df = df.reindex(sorted(df.columns), axis=1)
    df.insert(loc = 0, column = 'Iteration', value = np.arange(0, df.shape[0]), allow_duplicates=False)
    return df

Validation_Table = Search_List('val_',history.history.keys()) 
Train_Table = list(set( history.history.keys()) - set(Validation_Table))
Validation_Table = pd.DataFrame(np.array([history.history[x] for x in Validation_Table]).T, columns = Validation_Table)
Train_Table = pd.DataFrame(np.array([history.history[x] for x in Train_Table]).T, columns = Train_Table)
Validation_Table.columns = [x.replace('val_','') for x in Validation_Table.columns]

Train_Table = Table_modify(Train_Table)
Validation_Table = Table_modify(Validation_Table)

# Train Set Score
score = model.evaluate(train_ds, batch_size = batch_size, verbose = 0)
score = pd.DataFrame(score, index = model.metrics_names).T
score.index = ['Train Set Score']
# Validation Set Score
Temp = model.evaluate(val_ds, batch_size = batch_size, verbose = 0) 
Temp = pd.DataFrame(Temp, index = model.metrics_names).T
Temp.index = ['Validation Set Score']
score = score.append(Temp)
score.rename(columns= Metrics_Names, inplace = True)
score = score.reindex(sorted(score.columns), axis=1)
display(score.style.set_precision(4))

	Accuracy	Loss
Train Set Score	1.0000	0.0064
Validation Set Score	0.9698	0.2427

def Plot_history(history, PD, Title = False, metrics_names = [x.title() for x in model.metrics_names]):
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=[0.6, 0.4],
                        specs=[[{"type": "scatter"},{"type": "table"}]])
    # Left
    Colors = ['OrangeRed', 'MidnightBlue', 'purple']
    for j in range(len(metrics_names)):
        fig.add_trace(go.Scatter(x= history['Iteration'].values, y= history[metrics_names[j]].values,
                                 line=dict(color=Colors[j], width= 1.5), name = metrics_names[j]), 1, 1)
    fig.update_layout(legend=dict(x=0, y=1.1, traceorder='reversed', font_size=12),
                  dragmode='select', plot_bgcolor= 'white', height=600, hovermode='closest',
                  legend_orientation='h')
    fig.update_xaxes(range=[history.Iteration.min(), history.Iteration.max()],
                     showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    fig.update_yaxes(range=[0, PD['yLim']], showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    # Right
    if not PD['Table_Rows'] == None:
        ind = np.linspace(0, history.shape[0], PD['Table_Rows'], endpoint = False).round(0).astype(int)
        ind = np.append(ind, history.index[-1])
        history = history[history.index.isin(ind)]
    T = history.copy()
    T[metrics_names] = T[metrics_names].applymap(lambda x: '%.4e' % x)
    Temp = []
    for i in T.columns:
        Temp.append(T.loc[:,i].values)
    TableColors = PD['TableColors']
    fig.add_trace(go.Table(header=dict(values = list(history.columns), line_color=TableColors[0],
                   fill_color=TableColors[0], align=['center','center'], font=dict(color=TableColors[1], size=12), height=25),
                   columnwidth = PD['tablecolumnwidth'], cells=dict(values=Temp, line_color=TableColors[0],
                      fill=dict(color=[TableColors[1], TableColors[1]]),
                      align=['center', 'center'], font_size=12,height=20)), 1, 2)
    if Title != False:
        fig.update_layout(plot_bgcolor= 'white',
                      title={'text': Title, 'x':0.46, 'y':0.94, 'xanchor': 'center', 'yanchor': 'top'},
                          yaxis_title='Frequency')
    fig.show()

PD = dict(Table_Rows = 25, yLim = 1.2, tablecolumnwidth = [0.3, 0.4, 0.4], TableColors = ['Navy','White'])
Plot_history(Train_Table, Title = 'Train Set', PD = PD)
Plot_history(Validation_Table, Title = 'Validation Set', PD = PD)

Here, we only went through a few iterations; however, we need to train the model for more iterations to get more accurate results.

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References

1. TensorFlow Image
2. Image classification
3. Kaggle Fire Dataset
4. TensorFlow Docs

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